RT info:eu-repo/semantics/doctoralThesis
T1 Monitorización de la procalcitonina para evaluar el pronóstico en pacientes críticos con sepsis.
A1 Lózar De la Viña, Alicia de
A2 Universidad de Valladolid. Escuela de Doctorado
K1 Septicemia
K1 Procalcitonin
K1 Procalcitonina
K1 Sepsis
K1 Sepsis
K1 Algorithm
K1 Algoritmo
K1 Appropriate lab use
K1 Gestión del laboratorio
K1 32 Ciencias Médicas
AB Introduction Sepsis is an organ dysfunction caused by a dysregulated host response to infection. It is a serious clinical condition that compromises the patient’s life and represents a significant health problem within intensive care units (ICUs), being the leading cause of mortality in non–coronary ICUs. Procalcitonin (PCT) is a useful biomarker in this context, as many tissues can synthesize it during bacterial infection or sepsis. The objective of this study is to develop and validate an algorithm based on PCT monitoring to predict the prognosis of patients with sepsis.Materials and Methods This is a retrospective and prospective observational study conducted on 101 patients with suspected sepsis, carried out in the ICU of the Hospital Universitario Fundación Alcorcón and the ICU of the Hospital Universitario Infanta Leonor, both in the Community of Madrid. In the retrospective phase of the study, PCT results of patients admitted to the ICU between 2011 and 2012 were analyzed. In the prospective phase, PCT was determined at specific times as indicated by the algorithm, in patients with sepsis, from March 2018 to April 2019. The main variable of interest was 28–day mortality.Results In the retrospective study, 136 PCT results obtained within the first 24 hours of admission were analyzed from 58 patients. A first model was adjusted, revealing that patients with a poor prognosis experienced a significant increase of 4.7 ng/ml (95% CI: 2.5–7; p < 0.001) in PCT levels per hour, whereas patients with a good prognosis showed more stable PCT levels. Subsequently, a second model was adjusted using specific measurements taken at 6, 12, and 24 hours to determine the optimal time interval for patient monitoring. An algorithm was developed where an increase of more than 30% or 0.45 ng/ml was considered indicative of a poor prognosis, while a decrease of more than 30% between 6–12 hours or 12–24 hours indicated a good prognosis. This algorithm, to which an additional determination at 36 hours was added due to the 24-hour half-life of PCT, was prospectively validated in a cohort of 43 patients.In the validation study, out of 43 patients included, the algorithm classified 19 as having a good prognosis, 9 as having a poor prognosis, and 15 as undecided. This classification in relation to 28–day mortality was statistically significant (Fisher's exact test, p = 0.005). The algorithm demonstrated a sensitivity of 80.0% (95% CI: 28.4–99.5), specificity of 86.8% (95% CI: 71.9–95.6), a positive predictive value of 44.4% (95% CI: 13.7–78.8), and a negative predictive value of 97.0% (95% CI: 84.7–100.0) when comparing the group of patients classified as having a good prognosis or undecided versus those with a poor prognosis.Conclusions The algorithm developed based on early PCT monitoring in patients with sepsis provides prognostic information about these patients, distinguishing between those with a good prognosis and those with a poor prognosis, defined as 28–day mortality.
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/74637
UL https://uvadoc.uva.es/handle/10324/74637
LA spa
NO Escuela de Doctorado
DS UVaDOC
RD 04-abr-2025